13 Opportunities for using low-protein diets for weanling pigs to improve intestinal health
Bibliographic record
Abstract
Abstract Dietary manipulation with respect to crude protein (CP) content has been suggested as part of the overall strategy for the nutritional management of weanling pigs to improve intestinal health. This has focused on the use of low CP diets that are appropriately fortified with crystalline amino acids (AA). Use of low CP diets minimizes the amount of undigested dietary protein entering the large intestine and being subjected to bacterial fermentation. This is important because protein fermentation leads to the production of toxic metabolites and encourages the proliferation of pathogenic bacteria, thus causing enteric problems such as post-weaning diarrhea. There have been considerable efforts to elucidate the mechanisms underlying the potential benefits of feeding low CP diets to piglets. In addition to impacting the intestinal microbiome and its associated activities, it is clear that feeding a low CP diets interferes with the attachment of enterotoxigenic E. coli to the intestinal mucosa, thus minimizing its ability to cause disease. Another area of interest has been how use low CP diets in combination with other dietary manipulations to further enhance intestinal health in piglets. In this regards, existing evidence suggests that a low CP diet may be used in combination with other dietary interventions, such as probiotics and dietary fiber, to further enhance gut health outcomes in piglets. Also, addressing the potential reduction in piglet performance when feeding low CP diets by looking more into diet formulation to avoid deficiencies of essential AA or even some of non-essential AA, is critical for successful use low CP diets. Based on the available information, a reduction of dietary protein by four percentage units coupled with appropriate AA supplementation can be a useful dietary strategy to improve intestinal health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".